2022-04-03 22:17:18 +02:00
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import pandas as pd
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler
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wine = pd.read_csv('winequality-red.csv')
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X_train,X_rem,y_train,y_rem = train_test_split(wine.iloc[:,:-1],wine.iloc[:,-1], test_size=0.2, random_state=1,stratify=wine["quality"])
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X_valid, X_test, y_valid, y_test = train_test_split(X_rem,y_rem, test_size=0.5)
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2022-04-05 02:01:27 +02:00
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print("Wielkosc danych: train, test, valid:")
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2022-04-03 22:17:18 +02:00
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print(X_train.shape)
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print(X_valid.shape)
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print(X_test.shape)
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print("wine describe:")
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print(wine.describe())
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norm = MinMaxScaler()
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norm_fit = norm.fit(X_train)
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norm_X_train = norm_fit.transform(X_train)
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norm_X_test = norm_fit.transform(X_test)
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norm_X_valid = norm_fit.transform(X_valid)
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